State of The Art Optimizers

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Ranger (FastAI RAdamW + Lookahead) is currently the fastest optimizer.

By combining LARS / LAMB, RangerLars (or Over9000) is also used.

However, it has been shown empirically that RangerLars doesnt achieve good results in the long term.

So, Paratrooper is born!

https://arxiv.org/abs/1907.08610 [Lookahead Optimizer: k steps forward, 1 step back (2019)]

https://youtu.be/TxGxiDK0Ccc [Lookahead Optimizer: k steps forward, 1 step back | Michael Zhang (2020)]

https://arxiv.org/abs/1712.09913 [Visualizing the Loss Landscape of Neural Nets (2018)]

https://arxiv.org/abs/1904.00962 [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (2019)]

Lookahead Optimizer

Another optimizer which shocked the community was the Lookahead optimizer by none other than

the original Adam authors + Geoffery Hinton! FastAI also showcased the Lookahead optimizer + RAdam (Ranger)

is super fast and now considered the fastest optimizer in AI!

The authors even show the expected loss over many iterations vs SGDM.

Likewise, Lookahead works since many neural network landscapes are ironically convex!

Notice convex when skip connections are used. [Ie use skip connections in deep nets!]

Likewise my own results show Lookahead is super promising vs plain RAdamW or AdamW.

Ranger Optimizer

And finally, FastAI s latest advanced optimizer is the Ranger Optimizer! Essentially, we combine

Lookahead and RAdamW together!

FastAI also showcased that beta1 should be 0.95 and not 0.9 for better results.

Results of Ranger are above.

LAMB, LARS Optimizer

When training neural nets, each layer could require a different learning rate.

Thats when LAMB / LARS comes along! They compute the optimum adaptive learning rate

for each layer, and use this to update the final parameters.

Given the Adam Optimizer with weight decay:

We then compute the layer wise ratio R

And apply it layer wise

However, LAMB might actually overshoot, and attain worse accuracy overtime.

So, we have to clamp this ratio (as done in LARS).

RangerLars Optimizer

Also known as Over9000 or just RangerLars or RangerLamb, this optimizer is shockingly good!

(That is, for the first few epochs ☹)

It seems like the clipping down to bring the LARS / LAMB coefficient down is actually hurting the performance.

Instead of clipping at 0, I found clipping at 1 to be better.

However, RangerLars over many epochs tends to be extremely noisy.

Sadly, no literature was provided as to alleviate this issue. The good thing now is that

on just 25 epochs, RangerLars attains the same MSE as Ranger at 120 epcohs!!!

Thats a massive improvement.

Paratrooper Optimizer

So, we can alleviate these issues by clipping the maximum ratio of 10 over time!!

However, we dont just want to create some sort of hack to attain the maximum ratio.

We can utilise the Adam bias correction term.

Ie, if we use the reciprocal:

However, it’s still too low. So, we use the RAdam rectified ratio term in tandem:

Essentially, over time, the maximum trust ratio descends to 1, and returns back to being

pure Ranger updates.

Paratrooper essentially acts like RangerLars for the first few epochs, then reverts to becoming

Ranger for the last few epochs.

Strangely, Paratrooper attains even LOWER loss then RangerLars or Ranger, so clearly Paratrooper is a win!

So finally, combined with Ranger: the Paratrooper final algorithm:

One big advantage of Paratrooper is it fares better with the reduction of LR on plateau trick.

Its pretty clear the steps showcase learning rate decays when the loss plateaus.

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